Related Experiment Video
Updated: Jul 3, 2025
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Integrated Proteogenomic Analysis Reveals Distinct Potentially Actionable Therapeutic Vulnerabilities in
Pushpinder Kaur1,2, Alexander Ring3, Tania B Porras4
1Department of Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Abstract:
Triple-negative breast cancer (TNBC) is characterized by an aggressive clinical presentation and a paucity of clinically actionable genomic alterations. Here, we utilized the Cancer Genome Atlas (TCGA) to explore the proteogenomic landscape of TNBC subtypes to see whether genomic alterations can be inferred from proteomic data. We found only 4% of the protein level changes are explained by mutations, while 21% of the protein and 35% of the transcriptomics changes were determined by copy number alterations (CNAs). We found tighter coupling between proteome and genome in some genes that are predicted to be the targets of drug inhibitors, including CDKs, PI3K, tyrosine kinase (TKI), and mTOR. The validation of our proteogenomic workflow using mass spectrometry Clinical Proteomic Tumor Analysis Consortium (MS-CPTAC) data also demonstrated the highest correlation between protein-RNA-CNA. The integrated proteogenomic approach helps to prioritize potentially actionable targets and may enable the acceleration of personalized cancer treatment.
Insights
Triple-negative breast cancer (TNBC) research reveals proteogenomic insights. Copy number alterations significantly impact protein and transcript levels, guiding personalized cancer therapy development.
Area of Science:
- Oncology
- Genomics
- Proteomics
Background:
- Triple-negative breast cancer (TNBC) presents aggressively with limited actionable genomic targets.
- Understanding the interplay between genome and proteome is crucial for TNBC treatment.
Purpose of the Study:
- To explore the proteogenomic landscape of TNBC subtypes.
- To determine if genomic alterations can be inferred from proteomic data.
- To identify potential therapeutic targets through integrated analysis.
Main Methods:
- Utilized The Cancer Genome Atlas (TCGA) for proteogenomic analysis of TNBC.
- Analyzed protein, transcriptomic, and genomic data (mutations, copy number alterations).
- Validated findings using Clinical Proteomic Tumor Analysis Consortium (MS-CPTAC) mass spectrometry data.
Main Results:
- Only 4% of protein changes were linked to mutations.
- Copy number alterations (CNAs) explained 21% of protein and 35% of transcriptomic changes.
- Stronger genome-proteome coupling observed for drug target genes (CDKs, PI3K, TKIs, mTOR).
- Validation confirmed high correlation between protein, RNA, and CNA data.
Conclusions:
- Proteogenomic analysis provides a deeper understanding of TNBC.
- CNAs are significant drivers of proteomic and transcriptomic alterations in TNBC.
- Integrated proteogenomics can prioritize actionable targets for personalized cancer treatment acceleration.
